





'''simple docstring'''




import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler


class 					SCREAMING_SNAKE_CASE       (a__				):


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      ):

          '''simple docstring'''






          super().__init__()
          __A		:					List[Any]						=			value_function
          __A		:					str						=			unet
          __A		:					Tuple						=			scheduler
          __A		:					List[Any]						=			env
          __A		:					List[str]						=			env.get_dataset()
          __A		:					List[str]						=			{}
          for key in self.data.keys():
               try:
                    __A		:					Optional[int]						=			self.data[key].mean()
               except:  # noqa: E722
                    pass
          __A		:					str						=			{}
          for key in self.data.keys():
               try:
                    __A		:					Optional[int]						=			self.data[key].std()
               except:  # noqa: E722
                    pass
          __A		:					Optional[Any]						=			env.observation_space.shape[0]
          __A		:					List[Any]						=			env.action_space.shape[0]


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          return (x_in - self.means[key]) / self.stds[key]


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          return x_in * self.stds[key] + self.means[key]


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase):

          '''simple docstring'''






          if type(_UpperCAmelCase) is dict:
               return {k: self.to_torch(_UpperCAmelCase) for k, v in x_in.items()}
          elif torch.is_tensor(_UpperCAmelCase):
               return x_in.to(self.unet.device)
          return torch.tensor(_UpperCAmelCase		,      device=self.unet.device)


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          for key, val in cond.items():
               __A		:					List[Any]						=			val.clone()
          return x_in


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					List[str]						=			x.shape[0]
          __A		:					Union[str, Any]						=			None
          for i in tqdm.tqdm(self.scheduler.timesteps):
               # create batch of timesteps to pass into model
               __A		:					str						=			torch.full((batch_size,)		,      _UpperCAmelCase		,      device=self.unet.device		,      dtype=torch.long)
               for _ in range(_UpperCAmelCase):
                    with torch.enable_grad():
                         x.requires_grad_()

                         # permute to match dimension for pre-trained models
                         __A		:					Optional[int]						=			self.value_function(x.permute(0		,      2		,      1)		,      _UpperCAmelCase).sample
                         __A		:					int						=			torch.autograd.grad([y.sum()]		,      [x])[0]

                         __A		:					Optional[Any]						=			self.scheduler._get_variance(_UpperCAmelCase)
                         __A		:					int						=			torch.exp(0.5 * posterior_variance)
                         __A		:					int						=			model_std * grad

                    __A		:					Optional[Any]						=			0
                    __A		:					List[str]						=			x.detach()
                    __A		:					Tuple						=			x + scale * grad
                    __A		:					Optional[Any]						=			self.reset_xa(_UpperCAmelCase		,      _UpperCAmelCase		,      self.action_dim)

               __A		:					int						=			self.unet(x.permute(0		,      2		,      1)		,      _UpperCAmelCase).sample.permute(0		,      2		,      1)

               # TODO: verify deprecation of this kwarg
               __A		:					int						=			self.scheduler.step(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      predict_epsilon=_UpperCAmelCase)['prev_sample']

               # apply conditions to the trajectory (set the initial state)
               __A		:					Optional[int]						=			self.reset_xa(_UpperCAmelCase		,      _UpperCAmelCase		,      self.action_dim)
               __A		:					Dict						=			self.to_torch(_UpperCAmelCase)
          return x, y


     def __call__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase=64		,      _UpperCAmelCase=32		,      _UpperCAmelCase=2		,      _UpperCAmelCase=0.1):

          '''simple docstring'''






          __A		:					str						=			self.normalize(_UpperCAmelCase		,      'observations')
          __A		:					str						=			obs[None].repeat(_UpperCAmelCase		,      axis=0)

          __A		:					Any						=			{0: self.to_torch(_UpperCAmelCase)}
          __A		:					Any						=			(batch_size, planning_horizon, self.state_dim + self.action_dim)

          # generate initial noise and apply our conditions (to make the trajectories start at current state)
          __A		:					Tuple						=			randn_tensor(_UpperCAmelCase		,      device=self.unet.device)
          __A		:					Tuple						=			self.reset_xa(_UpperCAmelCase		,      _UpperCAmelCase		,      self.action_dim)
          __A		:					Tuple						=			self.to_torch(_UpperCAmelCase)

          # run the diffusion process
          __A					,__A		:					Any						=			self.run_diffusion(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)

          # sort output trajectories by value
          __A		:					Any						=			y.argsort(0		,      descending=_UpperCAmelCase).squeeze()
          __A		:					str						=			x[sorted_idx]
          __A		:					Dict						=			sorted_values[:, :, : self.action_dim]
          __A		:					Dict						=			actions.detach().cpu().numpy()
          __A		:					Dict						=			self.de_normalize(_UpperCAmelCase		,      key='actions')

          # select the action with the highest value
          if y is not None:
               __A		:					int						=			0
          else:
               # if we didn't run value guiding, select a random action
               __A		:					Any						=			np.random.randint(0		,      _UpperCAmelCase)

          __A		:					str						=			denorm_actions[selected_index, 0]
          return denorm_actions
import unittest

from transformers import BigBirdConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask


if is_flax_available():
   import jax

   from transformers.models.big_bird.modeling_flax_big_bird import (
       FlaxBigBirdForCausalLM,
       FlaxBigBirdForMaskedLM,
       FlaxBigBirdForMultipleChoice,
       FlaxBigBirdForPreTraining,
       FlaxBigBirdForQuestionAnswering,
       FlaxBigBirdForSequenceClassification,
       FlaxBigBirdForTokenClassification,
       FlaxBigBirdModel,
   )




class 					_UpperCAmelCase			(     unittest.TestCase  ):



 '''simple docstring'''




 def __init__(		self				:   List[Any]							, lowercase_				:   Union[str, Any]							, lowercase_				:   Union[str, Any]=2							, lowercase_				:   Dict=56							, lowercase_				:   str=True							, lowercase_				:   Optional[Any]=True							, lowercase_				:   List[Any]=True							, lowercase_				:   Union[str, Any]=True							, lowercase_				:   Any=99							, lowercase_				:   Optional[int]=32							, lowercase_				:   Tuple=2							, lowercase_				:   int=2							, lowercase_				:   List[str]=7							, lowercase_				:   Any="gelu_new"							, lowercase_				:   List[str]=0.1							, lowercase_				:   str=0.1							, lowercase_				:   List[Any]=512							, lowercase_				:   List[str]=16							, lowercase_				:   Optional[int]=2							, lowercase_				:   Union[str, Any]=0.02							, lowercase_				:   Union[str, Any]=4							, lowercase_				:   Union[str, Any]="block_sparse"							, lowercase_				:   Tuple=True							, lowercase_				:   Dict=False							, lowercase_				:   Dict=2							, lowercase_				:   Dict=3							, )						->	List[str]:


   """simple docstring"""







   _UpperCamelCase           =    parent
   _UpperCamelCase           =    batch_size
   _UpperCamelCase           =    seq_length
   _UpperCamelCase           =    is_training
   _UpperCamelCase           =    use_attention_mask
   _UpperCamelCase           =    use_token_type_ids
   _UpperCamelCase           =    use_labels
   _UpperCamelCase           =    vocab_size
   _UpperCamelCase           =    hidden_size
   _UpperCamelCase           =    num_hidden_layers
   _UpperCamelCase           =    num_attention_heads
   _UpperCamelCase           =    intermediate_size
   _UpperCamelCase           =    hidden_act
   _UpperCamelCase           =    hidden_dropout_prob
   _UpperCamelCase           =    attention_probs_dropout_prob
   _UpperCamelCase           =    max_position_embeddings
   _UpperCamelCase           =    type_vocab_size
   _UpperCamelCase           =    type_sequence_label_size
   _UpperCamelCase           =    initializer_range
   _UpperCamelCase           =    num_choices

   _UpperCamelCase           =    rescale_embeddings
   _UpperCamelCase           =    attention_type
   _UpperCamelCase           =    use_bias
   _UpperCamelCase           =    block_size
   _UpperCamelCase           =    num_random_blocks







 def 		__UpperCAmelCase				(		self				:   List[Any])						->	Dict:


   """simple docstring"""







   _UpperCamelCase           =    ids_tensor([self.batch_size, self.seq_length]							, self.vocab_size)

   _UpperCamelCase           =    None
   if self.use_attention_mask:
     _UpperCamelCase           =    random_attention_mask([self.batch_size, self.seq_length])

   _UpperCamelCase           =    None
   if self.use_token_type_ids:
     _UpperCamelCase           =    ids_tensor([self.batch_size, self.seq_length]							, self.type_vocab_size)

   _UpperCamelCase           =    BigBirdConfig(
       vocab_size=self.vocab_size							, hidden_size=self.hidden_size							, num_hidden_layers=self.num_hidden_layers							, num_attention_heads=self.num_attention_heads							, intermediate_size=self.intermediate_size							, hidden_act=self.hidden_act							, hidden_dropout_prob=self.hidden_dropout_prob							, attention_probs_dropout_prob=self.attention_probs_dropout_prob							, max_position_embeddings=self.max_position_embeddings							, type_vocab_size=self.type_vocab_size							, is_decoder=lowercase_							, initializer_range=self.initializer_range							, attention_type=self.attention_type							, block_size=self.block_size							, num_random_blocks=self.num_random_blocks							, use_bias=self.use_bias							, rescale_embeddings=self.rescale_embeddings							, )

   return config, input_ids, token_type_ids, attention_mask







 def 		__UpperCAmelCase				(		self				:   Optional[Any])						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase           =    self.prepare_config_and_inputs()
   _UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase      ,						_UpperCamelCase           =    config_and_inputs
   _UpperCamelCase           =    {
       "input_ids": input_ids,
       "token_type_ids": token_type_ids,
       "attention_mask": attention_mask,
   }
   return config, inputs_dict




@require_flax
class 					_UpperCAmelCase			(     lowerCAmelCase,			unittest.TestCase  ):



 '''simple docstring'''




 __A			       =					(
     (
         FlaxBigBirdForCausalLM,
         FlaxBigBirdModel,
         FlaxBigBirdForPreTraining,
         FlaxBigBirdForMaskedLM,
         FlaxBigBirdForMultipleChoice,
         FlaxBigBirdForQuestionAnswering,
         FlaxBigBirdForSequenceClassification,
         FlaxBigBirdForTokenClassification,
     )
     if is_flax_available()
     else ()
 )

 __A			       =					False
 __A			       =					False







 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	Optional[Any]:


   """simple docstring"""







   _UpperCamelCase           =    FlaxBigBirdModelTester(self)







 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 		__UpperCAmelCase				(		self				:   Union[str, Any])						->	List[str]:


   """simple docstring"""







   super().test_from_pretrained_save_pretrained()







 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 		__UpperCAmelCase				(		self				:   Any)						->	int:


   """simple docstring"""







   super().test_from_pretrained_with_no_automatic_init()







 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 		__UpperCAmelCase				(		self				:   List[Any])						->	Union[str, Any]:


   """simple docstring"""







   super().test_no_automatic_init()







 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 		__UpperCAmelCase				(		self				:   str)						->	Union[str, Any]:


   """simple docstring"""







   super().test_hidden_states_output()







 @slow
 def 		__UpperCAmelCase				(		self				:   Tuple)						->	Optional[int]:


   """simple docstring"""







   for model_class_name in self.all_model_classes:
     _UpperCamelCase           =    model_class_name.from_pretrained("google/bigbird-roberta-base")
     self.assertIsNotNone(lowercase_)







 def 		__UpperCAmelCase				(		self				:   Tuple)						->	str:


   """simple docstring"""







   if self.test_attn_probs:
     super().test_attention_outputs()







 @slow
 # copied from `test_modeling_flax_common` because it takes much longer than other models
 def 		__UpperCAmelCase				(		self				:   Tuple)						->	Dict:


   """simple docstring"""







   _UpperCamelCase      ,						_UpperCamelCase           =    self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     with self.subTest(model_class.__name__):
       _UpperCamelCase           =    self._prepare_for_class(lowercase_							, lowercase_)
       _UpperCamelCase           =    model_class(lowercase_)

       @jax.jit
       def model_jitted(lowercase_				:   Dict							, lowercase_				:   List[Any]=None							, **lowercase_				:   Tuple):
         return model(input_ids=lowercase_							, attention_mask=lowercase_							, **lowercase_)

       with self.subTest("JIT Enabled"):
         _UpperCamelCase           =    model_jitted(**lowercase_).to_tuple()

       with self.subTest("JIT Disabled"):
         with jax.disable_jit():
           _UpperCamelCase           =    model_jitted(**lowercase_).to_tuple()

       self.assertEqual(len(lowercase_)							, len(lowercase_))
       for jitted_output, output in zip(lowercase_							, lowercase_):
         self.assertEqual(jitted_output.shape							, output.shape)







 def 		__UpperCAmelCase				(		self				:   Any							, lowercase_				:   List[str]							, lowercase_				:   List[str]							, lowercase_				:   List[str]							, lowercase_				:   str=1e-5							, lowercase_				:   int="outputs"							, lowercase_				:   List[str]=None)						->	Tuple:


   """simple docstring"""







   if name.startswith("outputs.attentions"):
     return
   else:
     super().check_pt_flax_outputs(lowercase_							, lowercase_							, lowercase_							, lowercase_							, lowercase_							, lowercase_)




